A Consensus Study on the Competencies of Nurse Specialists Providing Care for Craniofacial and Cleft Lip and Palate Patients
Bibliographic record
Abstract
Currently, it is unclear which tasks and competencies nurse specialists provide within the European Reference Network for Craniofacial Anomalies and Ear, Nose, Throat Disorders (ERN CRANIO). This study aims to establish an expert consensus on the needed competencies for this nursing role. A narrative literature search on the role of nurses within cleft and craniofacial care was conducted. Statements on competencies and tasks were formed based on literature and expert opinion and categorized according to the Canadian Medical Education Directives for Specialists roles. A Delphi consensus process was conducted with nurse specialists from ERN CRANIO centers. A 9-point Likert Scale was used to measure consensus, which was defined as a mean score of ≥7 and ≤1 outliers. In total, 31 nurse specialists from 19 centers in 12 countries participated in the Delphi process. Overall, 105 statements on competencies and tasks achieved consensus. The domain "communicator" reached consensus on all items, while the domains "manager" and "scholar" perceived lesser and slower consensus. The nursing title, education, and nursing scope of practice revealed a great variety. This study provides valuable insight into the utilization of nursing roles with ERN CRANIO centers. Apart from demonstrating similarities, it also shows that national health care regulations and education pathways lead to different positions and (clinical) responsibilities of nurses. A European unification of nursing education and titles could ensure easier communication and collaboration between health professionals and might support a greater establishment of well-defined nurse specialist roles in ERN CRANIO centers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.102 | 0.198 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".